{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport random\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm_notebook\nimport IPython\nimport IPython.display\nimport PIL\nimport pickle\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":2,"outputs":[{"output_type":"stream","text":"['freesound-audio-tagging-2019', 'fat2019_prep_mels1']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"! ls ../input/fat2019_prep_mels1","execution_count":3,"outputs":[{"output_type":"stream","text":"mels_test.pkl\t\tmels_train_noisy.pkl\t    trn_noisy_best50s.csv\r\nmels_train_curated.pkl\tmels_trn_noisy_best50s.pkl\r\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## utils"},{"metadata":{"trusted":true},"cell_type":"code","source":"def seed_everything(seed):\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n\nSEED = 999\nseed_everything(SEED)","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def _one_sample_positive_class_precisions(scores, truth):\n    \"\"\"Calculate precisions for each true class for a single sample.\n\n    Args:\n      scores: np.array of (num_classes,) giving the individual classifier scores.\n      truth: np.array of (num_classes,) bools indicating which classes are true.\n\n    Returns:\n      pos_class_indices: np.array of indices of the true classes for this sample.\n      pos_class_precisions: np.array of precisions corresponding to each of those\n        classes.\n    \"\"\"\n    num_classes = scores.shape[0]\n    pos_class_indices = np.flatnonzero(truth > 0)\n    # Only calculate precisions if there are some true classes.\n    if not len(pos_class_indices):\n        return pos_class_indices, np.zeros(0)\n    # Retrieval list of classes for this sample.\n    retrieved_classes = np.argsort(scores)[::-1]\n    # class_rankings[top_scoring_class_index] == 0 etc.\n    class_rankings = np.zeros(num_classes, dtype=np.int)\n    class_rankings[retrieved_classes] = range(num_classes)\n    # Which of these is a true label?\n    retrieved_class_true = np.zeros(num_classes, dtype=np.bool)\n    retrieved_class_true[class_rankings[pos_class_indices]] = True\n    # Num hits for every truncated retrieval list.\n    retrieved_cumulative_hits = np.cumsum(retrieved_class_true)\n    # Precision of retrieval list truncated at each hit, in order of pos_labels.\n    precision_at_hits = (\n            retrieved_cumulative_hits[class_rankings[pos_class_indices]] /\n            (1 + class_rankings[pos_class_indices].astype(np.float)))\n    return pos_class_indices, precision_at_hits\n\n\ndef calculate_per_class_lwlrap(truth, scores):\n    \"\"\"Calculate label-weighted label-ranking average precision.\n\n    Arguments:\n      truth: np.array of (num_samples, num_classes) giving boolean ground-truth\n        of presence of that class in that sample.\n      scores: np.array of (num_samples, num_classes) giving the classifier-under-\n        test's real-valued score for each class for each sample.\n\n    Returns:\n      per_class_lwlrap: np.array of (num_classes,) giving the lwlrap for each\n        class.\n      weight_per_class: np.array of (num_classes,) giving the prior of each\n        class within the truth labels.  Then the overall unbalanced lwlrap is\n        simply np.sum(per_class_lwlrap * weight_per_class)\n    \"\"\"\n    assert truth.shape == scores.shape\n    num_samples, num_classes = scores.shape\n    # Space to store a distinct precision value for each class on each sample.\n    # Only the classes that are true for each sample will be filled in.\n    precisions_for_samples_by_classes = np.zeros((num_samples, num_classes))\n    for sample_num in range(num_samples):\n        pos_class_indices, precision_at_hits = (\n            _one_sample_positive_class_precisions(scores[sample_num, :],\n                                                  truth[sample_num, :]))\n        precisions_for_samples_by_classes[sample_num, pos_class_indices] = (\n            precision_at_hits)\n    labels_per_class = np.sum(truth > 0, axis=0)\n    weight_per_class = labels_per_class / float(np.sum(labels_per_class))\n    # Form average of each column, i.e. all the precisions assigned to labels in\n    # a particular class.\n    per_class_lwlrap = (np.sum(precisions_for_samples_by_classes, axis=0) /\n                        np.maximum(1, labels_per_class))\n    # overall_lwlrap = simple average of all the actual per-class, per-sample precisions\n    #                = np.sum(precisions_for_samples_by_classes) / np.sum(precisions_for_samples_by_classes > 0)\n    #           also = weighted mean of per-class lwlraps, weighted by class label prior across samples\n    #                = np.sum(per_class_lwlrap * weight_per_class)\n    return per_class_lwlrap, weight_per_class","execution_count":5,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## File/folder definitions\n\n- `df` will handle training data.\n- `test_df` will handle test data."},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"DATA = Path('../input/freesound-audio-tagging-2019')\nPREPROCESSED = Path('../input/fat2019_prep_mels1')\nWORK = Path('work')\nPath(WORK).mkdir(exist_ok=True, parents=True)\n\nCSV_TRN_CURATED = DATA/'train_curated.csv'\nCSV_TRN_NOISY = DATA/'train_noisy.csv'\nCSV_TRN_NOISY_BEST50S = PREPROCESSED/'trn_noisy_best50s.csv'\nCSV_SUBMISSION = DATA/'sample_submission.csv'\n\nMELS_TRN_CURATED = PREPROCESSED/'mels_train_curated.pkl'\nMELS_TRN_NOISY = PREPROCESSED/'mels_train_noisy.pkl'\nMELS_TRN_NOISY_BEST50S = PREPROCESSED/'mels_trn_noisy_best50s.pkl'\nMELS_TEST = PREPROCESSED/'mels_test.pkl'\n\ntrn_curated_df = pd.read_csv(CSV_TRN_CURATED)\ntrn_noisy_df = pd.read_csv(CSV_TRN_NOISY)\ntrn_noisy50s_df = pd.read_csv(CSV_TRN_NOISY_BEST50S)\ntest_df = pd.read_csv(CSV_SUBMISSION)\n\n#df = pd.concat([trn_curated_df, trn_noisy_df], ignore_index=True) # not enough memory\ndf = pd.concat([trn_curated_df, trn_noisy50s_df], ignore_index=True, sort=True)\ntest_df = pd.read_csv(CSV_SUBMISSION)\n\nX_train = pickle.load(open(MELS_TRN_CURATED, 'rb')) + pickle.load(open(MELS_TRN_NOISY_BEST50S, 'rb'))","execution_count":6,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Custom `open_image` for fast.ai library to load data from memory\n\n- Important note: Random cropping 1 sec, this is working like augmentation."},{"metadata":{"trusted":true},"cell_type":"code","source":"from fastai import *\nfrom fastai.vision import *\nfrom fastai.vision.data import *\nfrom fastai.callbacks import *\nimport random\n\nCUR_X_FILES, CUR_X = list(df.fname.values), X_train\n\ndef open_fat2019_image(fn, convert_mode, after_open)->Image:\n    # open\n    idx = CUR_X_FILES.index(fn.split('/')[-1])\n    x = PIL.Image.fromarray(CUR_X[idx])\n    # crop 1sec\n    time_dim, base_dim = x.size\n    crop_x = random.randint(0, time_dim - base_dim)\n    x = x.crop([crop_x, 0, crop_x+base_dim, base_dim])    \n    # standardize\n    return Image(pil2tensor(x, np.float32).div_(255))\n\nvision.data.open_image = open_fat2019_image","execution_count":7,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Follow multi-label classification\n\n- Almost following fast.ai course: https://nbviewer.jupyter.org/github/fastai/course-v3/blob/master/nbs/dl1/lesson3-planet.ipynb\n- But `pretrained=False`"},{"metadata":{"trusted":true},"cell_type":"code","source":"tfms = get_transforms(do_flip=True, max_rotate=0, max_lighting=0.1, max_zoom=0, max_warp=0.)\nsrc = (ImageList.from_csv(WORK, Path('..')/CSV_TRN_CURATED, folder='trn_curated')\n       .split_none()\n       .label_from_df(label_delim=',')\n)\ndata = (src.transform(tfms, size=128)\n        .databunch(bs=128).normalize(imagenet_stats)\n)","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.show_batch(3)","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x864 with 9 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def lwlrap(y_pred,y_true):\n    score, weight = calculate_per_class_lwlrap(y_true.cpu().numpy(), y_pred.cpu().numpy())\n    lwlrap = (score * weight).sum()\n    return torch.from_numpy(np.array(lwlrap))","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class MixUpCallback(LearnerCallback):\n    \"Callback that creates the mixed-up input and target.\"\n    def __init__(self, learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True):\n        super().__init__(learn)\n        self.alpha,self.stack_x,self.stack_y = alpha,stack_x,stack_y\n    \n    def on_train_begin(self, **kwargs):\n        if self.stack_y: self.learn.loss_func = MixUpLoss(self.learn.loss_func)\n        \n    def on_batch_begin(self, last_input, last_target, train, **kwargs):\n        \"Applies mixup to `last_input` and `last_target` if `train`.\"\n        if not train: return\n        lambd = np.random.beta(self.alpha, self.alpha, last_target.size(0))\n        lambd = np.concatenate([lambd[:,None], 1-lambd[:,None]], 1).max(1)\n        lambd = last_input.new(lambd)\n        shuffle = torch.randperm(last_target.size(0)).to(last_input.device)\n        x1, y1 = last_input[shuffle], last_target[shuffle]\n        if self.stack_x:\n            new_input = [last_input, last_input[shuffle], lambd]\n        else: \n            new_input = (last_input * lambd.view(lambd.size(0),1,1,1) + x1 * (1-lambd).view(lambd.size(0),1,1,1))\n        if self.stack_y:\n            new_target = torch.cat([last_target[:,None].float(), y1[:,None].float(), lambd[:,None].float()], 1)\n        else:\n            if len(last_target.shape) == 2:\n                lambd = lambd.unsqueeze(1).float()\n            new_target = last_target.float() * lambd + y1.float() * (1-lambd)\n        return {'last_input': new_input, 'last_target': new_target}  \n    \n    def on_train_end(self, **kwargs):\n        if self.stack_y: self.learn.loss_func = self.learn.loss_func.get_old()\n        \n\nclass MixUpLoss(nn.Module):\n    \"Adapt the loss function `crit` to go with mixup.\"\n    \n    def __init__(self, crit, reduction='mean'):\n        super().__init__()\n        if hasattr(crit, 'reduction'): \n            self.crit = crit\n            self.old_red = crit.reduction\n            setattr(self.crit, 'reduction', 'none')\n        else: \n            self.crit = partial(crit, reduction='none')\n            self.old_crit = crit\n        self.reduction = reduction\n        \n    def forward(self, output, target):\n        if len(target.size()) == 2:\n            loss1, loss2 = self.crit(output,target[:,0].long()), self.crit(output,target[:,1].long())\n            d = (loss1 * target[:,2] + loss2 * (1-target[:,2])).mean()\n        else:  d = self.crit(output, target)\n        if self.reduction == 'mean': return d.mean()\n        elif self.reduction == 'sum':            return d.sum()\n        return d\n    \n    def get_old(self):\n        if hasattr(self, 'old_crit'):  return self.old_crit\n        elif hasattr(self, 'old_red'): \n            setattr(self.crit, 'reduction', self.old_red)\n            return self.crit\n\ndef mixup(learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True) -> Learner:\n    \"Add mixup https://arxiv.org/abs/1710.09412 to `learn`.\"\n    learn.callback_fns.append(partial(MixUpCallback, alpha=alpha, stack_x=stack_x, stack_y=stack_y))\n    return learn\nLearner.mixup = mixup\n","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class ConvBlock(nn.Module):\n    def __init__(self, in_channels, out_channels):\n        super().__init__()\n        \n        self.conv1 = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, 3, 1, 1),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(),\n        )\n        self.conv2 = nn.Sequential(\n            nn.Conv2d(out_channels, out_channels, 3, 1, 1),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(),\n        )\n\n        self._init_weights()\n        \n    def _init_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight)\n                if m.bias is not None:\n                    nn.init.zeros_(m.bias)\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.zeros_(m.bias)\n        \n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = F.avg_pool2d(x, 2)\n        return x\n    \nclass Classifier(nn.Module):\n    def __init__(self, num_classes=1000): # <======== modificaition to comply fast.ai\n        super().__init__()\n        \n        self.conv = nn.Sequential(\n            ConvBlock(in_channels=3, out_channels=64),\n            ConvBlock(in_channels=64, out_channels=128),\n            ConvBlock(in_channels=128, out_channels=256),\n            ConvBlock(in_channels=256, out_channels=512),\n        )\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # <======== modificaition to comply fast.ai\n        self.fc = nn.Sequential(\n            nn.Dropout(0.2),\n            nn.Linear(512, 128),\n            nn.PReLU(),\n            nn.BatchNorm1d(128),\n            nn.Dropout(0.1),\n            nn.Linear(128, num_classes),\n        )\n\n    def forward(self, x):\n        x = self.conv(x)\n        #x = torch.mean(x, dim=3)   # <======== modificaition to comply fast.ai\n        #x, _ = torch.max(x, dim=2) # <======== modificaition to comply fast.ai\n        x = self.avgpool(x)         # <======== modificaition to comply fast.ai\n        x = self.fc(x)\n        return x","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def borrowed_model(pretrained=False, **kwargs):\n    return Classifier(**kwargs)\n\nf_score = partial(fbeta, thresh=0.1)\nlearn = cnn_learner(data, borrowed_model, pretrained=False, metrics=[lwlrap]).mixup(stack_y=False)\nlearn.unfreeze()\n","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.lr_find()\nlearn.recorder.plot(suggestion=True)","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}},{"output_type":"stream","text":"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\nMin numerical gradient: 2.29E-02\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(255, 1e-2,callbacks=[SaveModelCallback(learn, every='improvement', monitor='lwlrap', name='best')])","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>lwlrap</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.757308</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.705011</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.621783</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.496238</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.352765</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>0.236254</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>0.160765</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>0.119439</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>0.096854</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>0.084927</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>0.078775</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>0.074990</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>0.072265</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>0.069936</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>0.067865</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>0.066581</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>0.064878</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>0.063351</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>0.061763</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>0.060421</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>20</td>\n      <td>0.058779</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>21</td>\n      <td>0.057668</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>22</td>\n      <td>0.056354</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>23</td>\n      <td>0.055272</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>24</td>\n      <td>0.053909</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>25</td>\n      <td>0.053107</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>26</td>\n      <td>0.052405</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>27</td>\n      <td>0.051623</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>28</td>\n      <td>0.050923</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>29</td>\n      <td>0.050001</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>30</td>\n      <td>0.049068</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>31</td>\n      <td>0.049048</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>32</td>\n      <td>0.048198</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>33</td>\n      <td>0.047605</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>34</td>\n      <td>0.046605</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>35</td>\n      <td>0.046402</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>36</td>\n      <td>0.045834</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>37</td>\n      <td>0.045197</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>38</td>\n      <td>0.044743</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>39</td>\n      <td>0.044543</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>40</td>\n      <td>0.043771</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>41</td>\n      <td>0.043470</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>42</td>\n      <td>0.043032</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>43</td>\n      <td>0.042652</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>44</td>\n      <td>0.042251</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>45</td>\n      <td>0.041699</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>46</td>\n      <td>0.041413</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>47</td>\n      <td>0.041226</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>48</td>\n      <td>0.040431</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>49</td>\n      <td>0.039769</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>50</td>\n      <td>0.039587</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>51</td>\n      <td>0.039455</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>52</td>\n      <td>0.038587</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>53</td>\n      <td>0.038592</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>54</td>\n      <td>0.038329</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>55</td>\n      <td>0.037909</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>56</td>\n      <td>0.037420</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>57</td>\n      <td>0.037261</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>58</td>\n      <td>0.037090</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>59</td>\n      <td>0.036518</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>60</td>\n      <td>0.036312</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>61</td>\n      <td>0.036248</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>62</td>\n      <td>0.035829</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>63</td>\n      <td>0.035540</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>64</td>\n      <td>0.035120</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>65</td>\n      <td>0.034985</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>66</td>\n      <td>0.034645</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>67</td>\n      <td>0.034423</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>68</td>\n      <td>0.034387</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>69</td>\n      <td>0.033950</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>70</td>\n      <td>0.034065</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>71</td>\n      <td>0.033355</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>72</td>\n      <td>0.032856</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>73</td>\n      <td>0.032738</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>74</td>\n      <td>0.032387</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>75</td>\n      <td>0.032455</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>76</td>\n      <td>0.032193</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>77</td>\n      <td>0.031823</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>78</td>\n      <td>0.031627</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>79</td>\n      <td>0.031486</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>80</td>\n      <td>0.031348</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>81</td>\n      <td>0.030897</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>82</td>\n      <td>0.030619</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>83</td>\n      <td>0.030238</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>84</td>\n      <td>0.029982</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>85</td>\n      <td>0.029867</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>86</td>\n      <td>0.029467</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>87</td>\n      <td>0.029651</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>88</td>\n      <td>0.029490</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>89</td>\n      <td>0.028945</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>90</td>\n      <td>0.028891</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>91</td>\n      <td>0.028509</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>92</td>\n      <td>0.028521</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>93</td>\n      <td>0.028495</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>94</td>\n      <td>0.028097</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>95</td>\n      <td>0.028512</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>96</td>\n      <td>0.028135</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>97</td>\n      <td>0.027936</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>98</td>\n      <td>0.027717</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>99</td>\n      <td>0.027370</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>100</td>\n      <td>0.027379</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>101</td>\n      <td>0.026965</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>102</td>\n      <td>0.027213</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>103</td>\n      <td>0.027028</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>104</td>\n      <td>0.026743</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>105</td>\n      <td>0.026524</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>106</td>\n      <td>0.026643</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>107</td>\n      <td>0.026487</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>108</td>\n      <td>0.026219</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>109</td>\n      <td>0.026412</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>110</td>\n      <td>0.026019</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>111</td>\n      <td>0.025809</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>112</td>\n      <td>0.025946</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>113</td>\n      <td>0.025845</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>114</td>\n      <td>0.025619</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>115</td>\n      <td>0.025538</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>116</td>\n      <td>0.025461</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>117</td>\n      <td>0.025309</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>118</td>\n      <td>0.025041</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>119</td>\n      <td>0.024952</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>120</td>\n      <td>0.024835</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>121</td>\n      <td>0.024894</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>122</td>\n      <td>0.024850</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>123</td>\n      <td>0.024657</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>124</td>\n      <td>0.024309</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>125</td>\n      <td>0.024140</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>126</td>\n      <td>0.024276</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>127</td>\n      <td>0.024118</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>128</td>\n      <td>0.023941</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>129</td>\n      <td>0.024050</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>130</td>\n      <td>0.023963</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>131</td>\n      <td>0.023777</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>132</td>\n      <td>0.023904</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>133</td>\n      <td>0.023760</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>134</td>\n      <td>0.023473</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>135</td>\n      <td>0.023112</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>136</td>\n      <td>0.023355</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>137</td>\n      <td>0.023054</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>138</td>\n      <td>0.023047</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>139</td>\n      <td>0.023178</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>140</td>\n      <td>0.023090</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>141</td>\n      <td>0.022835</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>142</td>\n      <td>0.022792</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>143</td>\n      <td>0.022726</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>144</td>\n      <td>0.022680</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>145</td>\n      <td>0.022550</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>146</td>\n      <td>0.022630</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>147</td>\n      <td>0.022441</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>148</td>\n      <td>0.022410</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>149</td>\n      <td>0.022304</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>150</td>\n      <td>0.022205</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>151</td>\n      <td>0.022197</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>152</td>\n      <td>0.022226</td>\n      <td>#na#</td>\n      <td>00:13</td>\n    </tr>\n    <tr>\n      <td>153</td>\n      <td>0.022051</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>154</td>\n      <td>0.021926</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>155</td>\n      <td>0.021590</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>156</td>\n      <td>0.021715</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>157</td>\n      <td>0.021721</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>158</td>\n      <td>0.021798</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>159</td>\n      <td>0.021408</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>160</td>\n      <td>0.021488</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>161</td>\n      <td>0.021433</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>162</td>\n      <td>0.021364</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>163</td>\n      <td>0.021176</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>164</td>\n      <td>0.020950</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>165</td>\n      <td>0.020945</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>166</td>\n      <td>0.021083</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>167</td>\n      <td>0.021184</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>168</td>\n      <td>0.021152</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>169</td>\n      <td>0.021051</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>170</td>\n      <td>0.020856</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>171</td>\n      <td>0.020694</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>172</td>\n      <td>0.020556</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>173</td>\n      <td>0.020646</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>174</td>\n      <td>0.020459</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>175</td>\n      <td>0.020241</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>176</td>\n      <td>0.020231</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>177</td>\n      <td>0.020304</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>178</td>\n      <td>0.020072</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>179</td>\n      <td>0.020028</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>180</td>\n      <td>0.019920</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>181</td>\n      <td>0.020251</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>182</td>\n      <td>0.020290</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>183</td>\n      <td>0.020150</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>184</td>\n      <td>0.020090</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>185</td>\n      <td>0.019902</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>186</td>\n      <td>0.019947</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>187</td>\n      <td>0.019951</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>188</td>\n      <td>0.019837</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>189</td>\n      <td>0.019768</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>190</td>\n      <td>0.019756</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>191</td>\n      <td>0.019604</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>192</td>\n      <td>0.019486</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>193</td>\n      <td>0.019538</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>194</td>\n      <td>0.019541</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>195</td>\n      <td>0.019418</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>196</td>\n      <td>0.019248</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>197</td>\n      <td>0.019207</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>198</td>\n      <td>0.019346</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>199</td>\n      <td>0.019418</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>200</td>\n      <td>0.019275</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>201</td>\n      <td>0.019239</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>202</td>\n      <td>0.019111</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>203</td>\n      <td>0.019260</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>204</td>\n      <td>0.018996</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>205</td>\n      <td>0.019175</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>206</td>\n      <td>0.018868</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>207</td>\n      <td>0.019120</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>208</td>\n      <td>0.018857</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>209</td>\n      <td>0.019084</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>210</td>\n      <td>0.019053</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>211</td>\n      <td>0.018960</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>212</td>\n      <td>0.018900</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>213</td>\n      <td>0.018950</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>214</td>\n      <td>0.018858</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>215</td>\n      <td>0.018781</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>216</td>\n      <td>0.018703</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>217</td>\n      <td>0.018778</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>218</td>\n      <td>0.018465</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>219</td>\n      <td>0.018333</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>220</td>\n      <td>0.018500</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>221</td>\n      <td>0.018549</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>222</td>\n      <td>0.018559</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>223</td>\n      <td>0.018641</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>224</td>\n      <td>0.018427</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>225</td>\n      <td>0.018318</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>226</td>\n      <td>0.018343</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>227</td>\n      <td>0.018190</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>228</td>\n      <td>0.018211</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>229</td>\n      <td>0.018360</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>230</td>\n      <td>0.018374</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>231</td>\n      <td>0.018367</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>232</td>\n      <td>0.018317</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>233</td>\n      <td>0.018376</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>234</td>\n      <td>0.018428</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>235</td>\n      <td>0.018507</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>236</td>\n      <td>0.018324</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>237</td>\n      <td>0.018209</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>238</td>\n      <td>0.018410</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>239</td>\n      <td>0.018168</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>240</td>\n      <td>0.018295</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>241</td>\n      <td>0.018476</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>242</td>\n      <td>0.018361</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>243</td>\n      <td>0.018409</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>244</td>\n      <td>0.018400</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>245</td>\n      <td>0.018121</td>\n      <td>#na#</td>\n      <td>00:13</td>\n    </tr>\n    <tr>\n      <td>246</td>\n      <td>0.018236</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>247</td>\n      <td>0.018388</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>248</td>\n      <td>0.018244</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>249</td>\n      <td>0.018244</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>250</td>\n      <td>0.018421</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>251</td>\n      <td>0.018292</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>252</td>\n      <td>0.018386</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>253</td>\n      <td>0.018100</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>254</td>\n      <td>0.018063</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.lr_find()\nlearn.fit_one_cycle(50, 1e-2,callbacks=[SaveModelCallback(learn, every='improvement', monitor='lwlrap', name='best')])","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}},{"output_type":"stream","text":"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n    <div>\n        <style>\n            /* Turns off some styling */\n            progress {\n                /* gets rid of default border in Firefox and Opera. */\n                border: none;\n                /* Needs to be in here for Safari polyfill so background images work as expected. */\n                background-size: auto;\n            }\n            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n                background: #F44336;\n            }\n        </style>\n      <progress value='20' class='' max='50', style='width:300px; height:20px; vertical-align: middle;'></progress>\n      40.00% [20/50 04:16<06:24]\n    </div>\n    \n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>lwlrap</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.018885</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.018605</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.018593</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.018534</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.018277</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>0.018526</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>0.018913</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>0.019206</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>0.019449</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>0.019993</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>0.020537</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>0.020914</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>0.021219</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>0.021747</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>0.022062</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>0.022251</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>0.022150</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>0.022204</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>0.022010</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>0.022034</td>\n      <td>#na#</td>\n      <td>00:12</td>\n    </tr>\n  </tbody>\n</table><p>\n\n    <div>\n        <style>\n            /* Turns off some styling */\n            progress {\n                /* gets rid of default border in Firefox and Opera. */\n                border: none;\n                /* Needs to be in here for Safari polyfill so background images work as expected. */\n                background-size: auto;\n            }\n            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n                background: #F44336;\n            }\n        </style>\n      <progress value='16' class='' max='38', style='width:300px; height:20px; vertical-align: middle;'></progress>\n      42.11% [16/38 00:05<00:07 0.0220]\n    </div>\n    "},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.export()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Test prediction and making submission file simple\n- Switch to test data.\n- Overwrite results to sample submission; simple way to prepare submission file."},{"metadata":{"trusted":false},"cell_type":"code","source":"from fastai.core import *\nfrom fastai.basic_data import *\nfrom fastai.basic_train import *\nfrom fastai.torch_core import *\ndef _tta_only(learn:Learner, ds_type:DatasetType=DatasetType.Valid, num_pred:int=30) -> Iterator[List[Tensor]]:\n    \"Computes the outputs for several augmented inputs for TTA\"\n    dl = learn.dl(ds_type)\n    ds = dl.dataset\n    old = ds.tfms\n    aug_tfms = [o for o in learn.data.train_ds.tfms]\n    try:\n        pbar = master_bar(range(num_pred))\n        for i in pbar:\n            ds.tfms = aug_tfms\n            yield get_preds(learn.model, dl, pbar=pbar)[0]\n    finally: ds.tfms = old\n\nLearner.tta_only = _tta_only\n\ndef _TTA(learn:Learner, beta:float=0, ds_type:DatasetType=DatasetType.Valid, num_pred:int=30, with_loss:bool=False) -> Tensors:\n    \"Applies TTA to predict on `ds_type` dataset.\"\n    preds,y = learn.get_preds(ds_type)\n    all_preds = list(learn.tta_only(ds_type=ds_type, num_pred=num_pred))\n    avg_preds = torch.stack(all_preds).mean(0)\n    if beta is None: return preds,avg_preds,y\n    else:            \n        final_preds = preds*beta + avg_preds*(1-beta)\n        if with_loss: \n            with NoneReduceOnCPU(learn.loss_func) as lf: loss = lf(final_preds, y)\n            return final_preds, y, loss\n        return final_preds, y\n\nLearner.TTA = _TTA","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"del X_train\nX_test = pickle.load(open(MELS_TEST, 'rb'))\nCUR_X_FILES, CUR_X = list(test_df.fname.values), X_test\n\n\ntest = ImageList.from_csv(WORK, Path('..')/CSV_SUBMISSION, folder='test')\nlearn = load_learner(WORK, test=test)\npreds, _ = learn.TTA(ds_type=DatasetType.Test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"test_df[learn.data.classes] = preds\ntest_df.to_csv('submission.csv', index=False)\ntest_df.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.4"}},"nbformat":4,"nbformat_minor":1}